Financial Development, Sectoral Reallocation, and Volatility:International Evidence∗

Simone ManganelliEuropean Central Bank

Alexander Popov†

European Central Bank

Abstract

This paper studies how financial development affects the volatility of GDP growththrough the channel of sectoral reallocation. For 28 OECD countries over the period1970—2007, we construct a benchmark industrial portfolio that minimizes the economy’slong-term volatility for a given level of long-term labor productivity growth. We findthat financial development substantially increases the speed with which the observedindustrial composition of output converges toward the benchmark. To overcome endo-geneity concerns, we exploit sectoral sensitivities to financial deepening and exogenousliberalization events.JEL classification: E32, E44, G11, O16Keywords: Financial development, volatility, growth, diversification, mean-variance

effi ciency

∗We thank Luc Laeven for sharing with us a variety of data. For useful comments, we thank Geert Bekaert,Enrica Detragiache, Charles Engel (the editor), Gabriel Fagan, John Fernald, Pierre-Olivier Gourinchas, PhilippHartmann, Jean Imbs, Urban Jermann, Sebnem Kalemli-Ozcan, Dirk Krueger, Luc Laeven, Ross Levine, LeslieLipschitz, Florencio Lopez-de-Silanes, Valerie Ramey, Sergio Rebelo, Rafael Repullo, Helene Rey, Peter Tufano, twoanonymous referees, seminar participants at the ECB and the IMF, and conference participants at the 2010 FinancialIntermediation Research Society Meeting, the National Bank of Poland Conference "Heterogeneous Nations andGlobalized Financial Markets," the 2010 World Congress of the Econometric Society, and the 2011 Federal ReserveBank of Chicago Annual International Banking Conference. The opinions expressed herein are those of the authorsand do not necessarily reflect those of the ECB or the Eurosystem.†Corresponding author. European Central Bank, Financial Research Division, Sonnemannstrasse 22, D-60314

Frankfurt, email: Alexander.Popov@ecb.int

1. Introduction

A large empirical literature over the past two decades has documented important growth benefits

of financial development, but does higher growth come at the cost of increased economic volatility?

While frequent financial crises in both developing and developed countries seem to suggest that

the answer is "yes," the literature has identified two channels through which financial development

can in fact reduce growth volatility. The first is the stabilization of intrasectoral output. Braun

and Larrain (2005) and Raddatz (2006) use sectoral data on value added in large cross-sections

of countries, and find that financial development lowers output volatility, more so in financially

vulnerable sectors. As long as industrial shares and the correlations of sectoral output remain

constant, these results imply a reduction in overall volatility. Second, financial development can

induce an intersectoral reallocation of output away from sectors with a large contribution to aggre-

gate volatility. This argument relies on a portfolio optimization mechanism a la Markowitz (1952)

that exploits the correlations in sectoral returns across sectors. Using this approach, Acharya et

al. (2011) show that branching deregulation in the United States has reduced state business-cycle

volatility through a reallocation of output towards sectors with a large optimal weight implied by

mean-variance effi ciency.

This paper contributes to the literature by testing the second mechanism in an international

context. In theory, diversification of output through the channel of volatility-reducing reallocation

may not be a universal outcome of financial development if it depends on the superior institutional

features of a particular country (the United States). Our results strongly suggest that this is not

the case. Our approach is as follows. We first acquire data on output and employment for nine

sectors for 28 OECD countries starting in 1970. We use these data to construct, for each country,

a benchmark set of optimal sectoral employment shares, which minimizes long-term aggregate

volatility for a given level of long-term growth. In particular, a sector’s optimal share is derived from

an argument that depends on the sector’s own relative labor productivity and labor productivity

growth, as well as on the volatility and the correlation with other sectors thereof. We then estimate

the effect of financial development (captured in the main tests by the level of private credit to

GDP) over time on the speed with which the economy’s actual industrial composition converges

to the benchmark. The evidence strongly suggests that financial development has accelerated

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this convergence. A two-standard-deviation increase in financial development results in a roughly

0.6% higher annual speed of convergence towards the effi cient industrial composition. By means

of illustration, if in 1970 Italy had as deep credit markets as the United States, then in 2007 its

economy would have exhibited a sectoral composition associated with 10% lower volatility than the

realized one, for the same level of realized labor productivity level and growth.

We address a number of concerns about the interpretation and robustness of our main findings.

First, our results suggest that developed financial markets reduce long-term volatility by exploiting

the correlations across sectors in labor productivity level and growth, rather than by simply in-

creasing the weight of low-volatility sectors. An alternative mechanism implied by our results could

be the following: finance reallocates resources towards fast-growing sectors, and so they become

larger. Because large sectors are more stable, aggregate volatility declines over time. If this is the

case, the correlations in sectoral returns would be irrelevant for the evolution of aggregate volatility,

and we could simply be capturing a finance-induced reallocation towards (ex-post) low-volatility

sectors. However, we show that when in the construction of the optimal industrial portfolio we

artificially set the correlations across sectors to zero, the effect of financial development on the

speed of convergence disappears. This result sheds new light on how financial development affects

the economy. In particular, Wurgler (2000) argues that in financially developed economies boom-

ing sectors grow faster by generating higher investment, and Imbs (2007) shows that high-growth

sectors tend to have higher volatility. We argue that these results are not incompatible with lower

long-term aggregate volatility if at the same time output is reallocated away from sectors with a

large contribution to aggregate volatility through the growth correlations mechanism.

The second concern is methodological. In the calculations of the mean-variance effi ciency fron-

tier, we implicitly assume that there are no structural breaks in the underlying stochastic process

generating the unconditional frontier. While this can be true for economies with mature financial

markets, many of the countries in our sample underwent financial liberalization during our sample

period, possibly inducing a structural break in the sectoral returns. We account for this possibility

by repeating our tests on a subsample of countries that liberalized their financial markets prior to

the start of the sample period. We also calculate benchmark industrial allocations for more than

one period per country (before and after the start of the "Great Moderation"). Our main estimates

are qualitatively unaltered by these alternative approaches.

2

Third, our results might be biased by a demand-driven move over the development cycle towards

sectors with lower intrinsic volatility, like health provision, education, and government services

(Koren and Tenreyro, 2007). They also could be related to the increase in size of the service sector

fueled by a finance-promoted shift towards more capital-intensive technologies (Larrain, 2010). In

that regard, the estimated positive effect of finance on convergence toward the benchmark allocation

might be biased by a preference-driven or a technology-driven global move away from intrinsically

volatile sectors. We address this concern by employing a panel specification with industry-year

and country-industry fixed effects. This accounts for convergence-affecting mechanisms that are

time-invariant for each sector in each country or that display sector-specific trends. Consequently,

we are able to isolate the contribution of the time-varying country-specific component of finance to

convergence.

Fourth, our estimates can be contaminated by omitted variables bias and reversed causality. For

example, unobserved risk aversion or propensity to save might be driving both output reallocation

and financial development. Alternatively, if financial services have a "luxury good" component,

richer and better diversified economies would demand more of them. We address these concerns in

a number of ways. First, in the spirit of Rajan and Zingales (1998), we exploit the variation across

sectors in natural technological dependence on external finance, and show that convergence is faster

for sectors that are naturally sensitive to credit market development. We also replace our continuous

measure of financial development with dummy variables proxying for financial liberalization. This

de jure measure is largely exogenous (Bekaert et al., 2005) and so it should additionally address

concerns about the endogeneity of financial development. Finally, we show that convergence is

at play in both capital-intensive and labor-intensive sectors, assuaging concerns about our results

being driven by the fact that countries that are better diversified and at the same time derive a

larger share of economic output from more capital intensive industries can demand larger financial

sectors.

Our results inform the literature on the effect of financial development on economic volatility.

For example, Hellmann et al. (2000) argue that financial development fuels competition and erodes

banks’franchise value, thus incentivizing banks to take on more risk. Since governments cannot

commit to not provide bailouts in times of crises, banks have incentives to gamble for resurrec-

tion, exacerbating the business cycle. Alternatively, financial development can reduce volatility by

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alleviating information asymmetries, thus reducing the role of borrower’s net worth in the amplifi-

cation of shocks (Aghion et al., 1999; Caballero and Krishnamurty, 2001).1 Empirical work using

various sample periods and proxies for financial development has presented evidence to both ends.

For instance, Easterly et al. (2000) find that financial development reduces output volatility, and

Bekaert et al. (2006) find that financial liberalization reduces consumption volatility. At the same

time, Kaminsky and Reinhart (1999) link credit growth to crises, and Beck et al. (2006) find no

correlation between financial development and long-term volatility. Using sectoral data, Braun and

Larrain (2005), Larrain (2006), and Raddatz (2006) present evidence that financial development

lowers output volatility in manufacturing industries with high external dependence and liquidity

needs. However, Levchenko et al. (2009) show that financial liberalization increases volatility, more

so in financially vulnerable sectors. We contribute to this literature by estimating a robust nega-

tive association between financial development and aggregate volatility in a large cross-section of

countries and by demonstrating the link between the reduction in volatility and the finance-driven

evolution of the economy’s industrial composition.

We also relate to a vast empirical literature on the finance and growth nexus.2 This literature

documents a significant, positive, causal effect of finance on economic growth, both at the country

level (e.g., Levine and Zervos, 1998; Beck et al., 2000; Bekaert et al., 2005) and at the sector level

(e.g., Rajan and Zingales, 1998; Fisman and Love, 2007; Gupta and Yuan, 2009).3 This literature

usually abstracts from the effect of finance on volatility. In comparison, we use a mean-variance

effi ciency approach to study how financial development affects growth and volatility simultaneously.

Finally, our paper is related to a growing body of literature that has focused on the link between

economic growth and volatility of growth. From a theoretical point of view, the link is ambiguous.

For example, endogenous growth is affected by business-cycle volatility negatively in the presence of

diminishing returns to investment, and positively in the presence of precautionary savings, creative

destruction, liquidity constraints, or high-return high-risk technologies. The combined evidence

implies that growth and volatility tend to relate negatively at the country level (Ramey and Ramey,

1 In general, the effect of finance on the variability of output is expected to vary depending on whether monetaryor real shocks are at play (Bachetta and Caminal, 2000) and on whether the real shocks are due to shifts in creditdemand or in credit supply (Morgan et al., 2004).

2The idea to link finance and growth in a causal way traces back to Schumpeter (1912) and later Goldsmith(1969) and McKinnon (1973), but the modern impetus for studying the nexus is usually attributed to King andLevine (1993a, 1993b).

3For recent surveys, see Beck et al. (2001), Wachtel (2001), and Levine (2005).

4

1995),4 but positively at the industry level (Imbs, 2007). This apparent contradiction is resolved

by noticing that the positive correlation between risk and return at the sector level is more than

compensated in the aggregate by the negative correlations between sectoral growth rates. This

approach of distinguishing between the country-specific and sector-specific elements of volatility

relates to a seminal contribution by Koren and Tenreyro (2007), who show that in large part the

reduction of country-specific volatility over the development cycle is due to the reallocation of

output to sectors with intrinsically lower volatility. Our paper contributes to this line of research

in two important ways. First, we show that financial development is an important driver of the

reduction of country-specific volatility. Second, we argue that a large portion of the reduction in

volatility over the development cycle comes from a reallocation across sectors rather than from a

reduction in intrasectoral volatility.

The rest of the paper is structured in the following way. Section 2 presents our empirical

methodology and describes the data. Section 3 presents the empirical results together with en-

dogeneity and robustness tests. Section 4 concludes with a discussion of the main results and of

possible extensions.

2. Empirical methodology

2.1. Economic interpretation of mean variance utility optimization

Ignoring consumption-saving decisions, assume that a representative agent chooses the sectoral

employment shares in the economy, lt, to maximize a Constant Relative Risk Aversion (CRRA)

utility function:

max{lt}∞t=0

E0

∞∑t=1

βtU(Ct) = E0

∞∑t=1

βtC1−γt

1− γ , (1)

s.t.

Ct+1 = Yt+1(lt), ∀t. (2)

β is the discount rate, and γ > 1 is the coeffi cient of relative risk aversion. Yt+1 is the random

flow of per capita income at t+1. lt =

[l1t, l2t, ..., lSt

]′is a vector capturing relative sectoral

employment lst = LstLt, for each sector s ∈ {1, ..., S}, where Lst is total employment at time t in

4Empirical research on the link between GDP growth and volatility as a rule abstracts from the role of financialdevelopment. In an important deviation from this rule, Kose et al. (2006) show that financial integration hasweakened the negative relationship between growth and volatility.

5

sector s and Lt is total aggregate employment at time t. By definition,∑S

s=1 lst = 1.

Define Yt+1 = Yt exp{yt+1}, where yt+1 is the exponential rate of growth of per capita income.

We now link yt+1 to fundamental factors, by first writing per capita income (or output per worker)

as a function of sectoral employment shares (lst) and sectoral labor productivity (Ys,t+1):

Yt+1 =S∑s=1

lstYs,t+1, (3)

where employment is decided at time t. Assume that sectoral labor productivity grows at the rate

ys,t+1 such that

Ys,t+1 = Yst exp{ys,t+1}. (4)

We also assume that the growth rate ys,t+1 is independent of Yst, as this allows breaking down the

utility maximization into period-by-period maximization. Using the approximations lnX ≈ X − 1

and exp {X} = X + 1, the rate of growth can be written as:

yt+1 = ln(Yt+1/Yt)

= ln∑S

s=1 lstYstYtexp{ys,t+1}

≈∑S

s=1 lstYstYtexp{ys,t+1} − 1

≈∑S

s=1 lstYstYt(ys,t+1 + 1)− 1.

(5)

Denote xs,t+1 ≡ YstYt(ys,t+1 + 1). By construction, xs,t+1 includes both a level component of

relative labor productivity and a growth component of labor productivity. We assume that xt+1 =[x1,t+1, x2,t+1, ..., xS,t+1

]′is normally distributed:

xt+1 ∼ N (µ,Σ) . (6)

Note that if a random variable X is normally distributed, X ∼ N(µ, σ2), then by the properties

of lognormality, E[exp(X)] = exp(µ+ 1

2σ2). We also note that Y 1−γt+1 = (Yt exp{yt+1})1−γ =

6

exp{(1− γ) ln(Yt) + (1− γ)yt+1}. Then, using (5), expected utility can be rewritten as:

Et [U(Yt+1(lt))] = Et

[Y 1−γt+1

1− γ

](7)

= Et

[exp{(1− γ) ln(Yt) + (1− γ)yt+1}

1− γ

]=

exp{(1− γ) ln(Yt) + (1− γ)l′tµ+ (1− γ)2 12 l′tΣlt−(1− γ)}

1− γ .

Because 1 − γ < 0, by monotonicity, maximizing Et [U(Yt+1(lt))] is equivalent to minimizing

the function

U(lt;µ,Σ) = (1− γ) ln(Yt) + (1− γ)l′tµ+ (1− γ)21

2l′tΣlt−(1− γ). (8)

Neglecting the constants and Yt, which is known at time t, and dividing by (1− γ), the repre-

sentative agent’s optimization problem becomes:

maxlt

l′tµ− 12(γ − 1)l

′tΣlt. (9)

The coeffi cient multiplying the variance term is positive; therefore, (9) is a standard mean

variance problem, where the choice variable is relative employment in each sector s and the random

variable is proportional to the rate of productivity growth.

2.2. Constructing the optimal allocation benchmark

The program (9) is a standard mean-variance effi ciency (MVE) problem in the spirit of Markowitz

(1952). It boils down to computing optimal sector-specific employment shares that would minimize

distance to the MVE frontier. In principle, it would be possible to compute a time-varying, condi-

tional effi cient frontier, for instance, by modeling the variance covariance matrix with a multivariate

GARCH model. However, since we are interested in the long-run growth and risk opportunities

of the economy, it is more appropriate to use the unconditional means and variances. Both ap-

proaches rest on the implicit assumption that there are no structural breaks in the underlying

stochastic process.

To circumvent the dependence of the effi cient benchmark on the coeffi cient γ, we reformulate

7

the optimization problem in the following way, for each country c:

∥∥∥∥∥∥∥∥∥∥∥∥∥∥

minlct

l′ctΣclct

s.t. l′ctµc ≥ l′ctµc

lct ≥ 0∑Ss=1 lcst = 1,

(10)

where we have added an additional subscript for country, relative to the notation so far. lc,t

denotes the vector of observed employment shares for country c at time t. The nonnegativity

constraint reflects the fact that it is not economically meaningful to have negative weights for the

employment shares in this context. This optimization programme delivers the point on the frontier

that minimizes the country’s volatility of an argument, which is proportionate to relative labor

productivity and labor productivity growth, for the realized level of relative labor productivity and

labor productivity growth.5 The distance between such a point and the actual levels of volatility

can be interpreted as a measure of allocative effi ciency, because it measures by how much a country

could have reduced its macroeconomic volatility, while achieving the same level of growth, by simply

allocating differently its resources across sectors.

Denoting the vector solution to this problem by l∗c,t, and by l∗c,s,t the individual elements of this

vector, we can construct the following measure of country’s allocative effi ciency:

Dc,s,t = |l∗c,s,t − lc,s,t|, (11)

where lc,s,t are the observed actual allocations. Dc,s,t is the distance between optimal and actual

employment shares for each sector component of a country at time t.

A potential problem with the framework we employ is the strong assumption that labor pro-

ductivity itself is not affected by reallocation (i.e., it is exogenous to the sectoral composition).

However, it has a number of advantages compared with the framework used by Acharya et al.

(2011), who use growth in value added (rather than in labor productivity) to calculate the MVE

sectoral allocation. In particular, in their framework the growth rate of value added is mismeasured

5The variance-covariance matrix is not invertible when T<=N, and even when T is only slightly larger than N,the variance-covariance matrix is imprecisely estimated. Throughout the paper, we use an industrial classification,for which industries are aggregated at a level suffi cient to give precise estimates of the variance-covariance matrix.

8

by construction because it already includes sectoral reallocation (moving a worker from sector A

to sector B increases value added in sector B by the labor productivity of that sector).6 This

alternative mechanism can yield the tautological prediction that high-growth sectors become larger

over time or potentially converge faster to the MVE frontier. Nevertheless, in one of our robustness

checks, we derive the distance defined in (11) from a version of the optimization program (10),

where we use growth in value added instead of growth in value added per worker.

Figures 1, 2, and 3 illustrate three different growth-volatility profiles over time. The actual

industrial composition in the United States (Figure 1) and in the euro area (Figure 2) has strongly

converged over time toward the benchmark allocation in the volatility dimension, while the Japanese

economy experienced steady divergence throughout the sample period (Figure 3). We also note a

mechanical property of mean-variance effi ciency: the actual industrial composition in a number of

countries (such as Italy) lies fully under the tip of the MVE frontier, and so in these cases distance

to frontier in the volatility dimension coincides with distance to the minimum variance portfolio

(the tip of the frontier).

2.3. Finance and convergence: Empirical model

We study the link between finance and the economy’s growth-volatility profile using a standard

convergence framework. Our convergence test estimates the speed with which the actual employ-

ment share of sector s in country c converges to its optimal share in financially more developed

countries. This allows us to directly look into the issue of reallocation and examine which sectors

move faster to their implied optimal weights following financial development. Formally, we estimate

the following convergence equation:

Dc,s,t = αDc,s,t−1 + βDc,s,t−1 · Financec,t + γFinancec,t + δφcs + ηφst + εc,s,t, (12)

where Financec,t is equal to a standard measure of beginning-of-period financial market develop-

ment, and Dc,s,t is defined as in (11).7 Our coeffi cient of interest is β: if β < 0, then greater

6See Caselli (2005) on how reallocating from less productive to more productive sectors may affect aggregateproductivity.

7 It is important to note that (12) can be rewritten as

Dc,s,t = αDc,s,t−1 + (βDc,s,t−1 + γ) · Financec,t + δc · φs + ηt + εc,s,t,

and so the full effect of finance on distance to the allocative effi ciency frontier is given by βDc,s,t−1+γ. For example,

9

financial development is associated with faster convergence toward the benchmark allocation.8 The

inclusion of country-sector fixed effects (φcs) allows us to net out any unobservable country-sector

specific time-invariant influences (such as the technological specificity of the oil extraction industry

in Norway). The inclusion of industry-year fixed effects (φst) allows us to purge our estimates

from the effect of demand-driven or technology-driven industry-specific trends (for example, in the

context of the "Great Moderation"). We thus aim to isolate the within-country effect of financial

development.9

The relationship between financial market size and the economy’s growth-volatility profile is

illustrated in Figure 4, which plots each individual country’s autoregressive annual speed of con-

vergence to the benchmark industrial allocation over the sample period against its initial ratio

of private credit to GDP, for the cross-section of OECD countries.10 Clearly, the correlation is

strongly positive. Countries with initially deeper credit markets – typically Anglo-Saxon ones –

experienced a larger annual reduction in distance to the optimally diversified benchmark over the

past four decades than did less financially developed countries (typically Mediterranean and post-

communist economies). Thirteen percent of the cross-country variation in the speed of convergence

toward the benchmark industrial allocation is explained by the size of financial markets.

There are two conceptual issues with our empirical framework. First, while β < 0 in (12) would

indicate faster convergence toward the MVE frontier in the volatility dimension, it is still possible

that financially developed countries are simply converging faster to a higher level of distance.

Denoting by Dc,s the steady-state level of distance to MVE frontier and by C the sum of fixed

effects, (12) can be rearranged as:

if both β and γ are negative, then more finance decreases distance to frontier, but if β < 0 and γ > 0, then the totaleffect of finance depends on Dc,s,t−1, and for low levels of Dc,s,t−1, finance could lead to divergence even if β < 0.

8As pointed out by Acharya et al. (2011), the frontier is estimated with an error, and hence there is an attenuationbias in estimating convergence. This works against finding an effect and hence what we see in the data should beinterpreted as a lower bound for the true effect. In addition, as shown by Jagannathan and Ma (2003) in the contextof mean-variance allocation, imposing nonnegative constraints significantly reduces the impact of estimation error.

9We have estimated the equivalent of Equation (12) using country-level aggregates toward the optimal benchmark.This results in insignificant coeffi cients at the standard condifence levels. Our conjecture is that this is due to theloss of power associated with the considerably reduced sample size.10We define the autoregressive annual speed of convergence as 1 − αc, where αc denotes the estimate from the

regression

Dc,t = αcDc,t−1 + εc,t

for each country c in the sample, where Dc,t =19

√√√√ 9∑s=1

(Dc,s,t)2 for the nine sectors in our sample.

10

Dc,s =γFinancec,t + C

1− α− βFinancec,t.

Immediately,

∂Dc,s

∂Financec,t=

γ(1− α) + β · C(1− α− βFinancec,t)2

.

The sign of the derivative, provided β < 0 and given that α < 1, is indeterminate. In general, it

is possible for faster-converging countries (|β| large) to converge to a higher steady-state distance

to frontier as long as they start farther from the frontier (γ > 0 and large) and their autoregressive

speed of convergence α is not too high. This example shows that the question of the relationship

between financial development and steady-state distance to frontier is an empirical one. Because

we do not know the value of C, we cannot calculate the steady-state distance implied by our

regression coeffi cients, but we can conjecture that each country’s final distance to the MVE frontier

is a crude approximation of the steady-state distance. In Figure 5, we plot final distance to frontier

in our sample against beginning-of-period financial development. There is no discernible statistical

association between the two, and less than 1% of the cross-country variation in the final distance

to the benchmark industrial allocation is explained by the size of financial markets.

Second, we are assuming that the growth rate of labor productivity is measured correctly.

In the presence of measurement error that varies systematically across countries (for example, if

measurement error is lower in financially developed countries), our results might be biased. This

is a caveat we need to acknowledge; at the same time, by including only OECD countries in the

sample, we do make sure that measurement error is minimized. For example, Johnson et al. (2013)

show that although the within-country difference in measured GDP growth rates between various

revisions of the World Penn Tables is on average close to 2% for the rest of the world, it is only

0.1% for the sample of OECD countries.

We address the issue of the endogeneity of financial development in two alternative ways. First,

we replace our continuous measure of finance with dummies equal to one after the year in which

domestic financial markets were liberalized. It is commonly believed that policy decisions are more

exogenous than volume measures of finance (Bekaert et al., 2005). Second, we employ the Rajan

and Zingales (1998) approach of interacting our measure of finance with sector-specific proxies

11

for technological sensitivity to financial development, namely, "natural" dependence on external

finance and "natural" share of young firms. By identifying one channel via which finance should

speed convergence, we aim to purge the possible bias in our estimates induced by simultaneity. We

also show that convergence happens for both capital-intensive and labor-intensive sectors, ruling

out the possibility that countries that are better diversified and at the same time derive a larger

share of economic output from more capital-intensive industries can demand larger financial sectors.

2.4. Data

To compute levels of relative labor productivity and labor productivity growth rates at the

sectoral level, we employ data on nominal value added – which we deflate to get real values –

and on employment from the STAN Database for Structural Analysis. The data cover 28 countries

starting at best in 1970.11 The data are decomposed into nine SIC 1-digit sectors. Although

we lose substantial sectoral variation with nine industries, disaggregating the data by SIC 1-digit

industries serves two important purposes. For one, we thus make sure that we do not include

sectors with negligible employment share in the calculation of the benchmark allocation of output

across sectors. Second, the MVE calculations hinge on a dimensionality restriction, namely, that

the number of years of data available should be higher than the number of sectors. Thus, we are

unable to construct benchmark output allocations for countries for which data start after 1987 if

we focus on a larger set of 2-digit industries. It is also worth noting that, if anything, aggregation

into a set of so coarsely defined sectors makes it harder rather than easier to detect an effect of

finance on the reallocation of resources across economic activities.12

Two data clarifications are in order. First, the level of disaggregation follows arbitrary statistical

conventions, for which reason some activities are recorded more coarsely than others. If the economy

tends to specialize, at later stages of development, in sectors that are more finely recorded, a

mechanical relation between financial development and diversification can emerge. Second, while

UNIDO has been the preferred dataset in the finance and growth literature, it only includes data

11Coverage varies across countries. While for the majority of the countries (16) the data start in the 1970s, foreight countries (Czech Republic, Germany, Greece, Hungary, Iceland, Poland, Slovakia, and Switzerland) they onlystart in the 1990s.12For each country-sector-year, data on labor productivity, relative labor productivity for each country-year, and

on the annual growth rate of labor productivity, are used to calculate the empirical counterpart to xs,t+1 defined inSection 2.1. It is worth noting that most of the variation in xs,t+1 comes from variations in relative labor productivityrather than from variations in the growth rate of sectoral labor productivity.

12

on the manufacturing sector, and so STAN is more suited to studying optimal reallocation in the

context of the major shift during our sample period from manufacturing towards services.

The financial variables used in this paper come from two different sources. The main measure

of financial markets development is private credit / GDP. The value of total credits by financial

intermediaries to the private sector goes into the numerator (lines 22d and 42d in the International

Financial Statistics), and so this measure excludes credits issued by the central banks. The reason

for this exclusion is that in many cases the latter is likely to be determined by political considerations

rather than by economic considerations. The variable also excludes credit to the public sector and

cross-claims of one group of intermediaries on another. Finally, it counts credit from all financial

institutions rather than counting only deposit money banks. The data on this variable come from

Beck et al. (2013) and are available for all 28 countries in the data set.

While the main measure of domestic financial development considered in the paper is ubiquitous

in empirical research, it is intrinsically likely to contain measurement error. It is diffi cult to capture

all aspects of financial development in one empirical proxy. Moreover, there are idiosyncratic

differences across countries in the availability of unobservable sources of working capital, such as

trade credit or family ownership. To confront these issues, we use in robustness tests data on equity

market size (stock market capitalization / GDP), bond market size (private + public bond market

capitalization / GDP), as well as various measures of financial integration.

We also address the issue of the endogeneity of any volume measure of finance to economic

development by employing a de jure measure of financial development in addition to the de facto

measure. In practice, we replace private credit / GDP with information on banking sector liber-

alization dates. This alternative indicator is constructed by assigning a value of zero for the years

in which the country’s domestic credit market was not liberalized, and one for the years after it

became liberalized. The indicator comes from Bekaert et al. (2005).13

Table 1 summarizes average actual and optimal sectoral employment shares for the nine SIC

1-digit sectors in the dataset. We find that three of the nine sectors ("Manufacturing;" "Wholesale

and Retail Trade and Restaurants and Hotels;" and "Community, Social, and Personal Services")

together account for 68% of the "optimal" sectoral portfolio implied by long-term labor productivity

13See Appendix Table 1 for data on private credit and for credit market liberalization events. See Appendix TableA7 for all variables and sources.

13

growth, volatility, and cross-sectoral correlations. Our estimates also imply that the actual share

of a sector can be considerably higher than the optimal share. For example, "Finance, Insurance,

Real Estate, and Business Services" accounts for a ninth of overall employment, whereas in an

MVE-effi cient world it should only account for 3.1% on average.

In Table 2, we look at the country-specific discrepancy between actual and optimal sectoral

weight, for the same nine SIC 1-digit sectors in the dataset. The table uncovers striking differences

across sectors and countries between actual and MVE-implied industrial composition. For exam-

ple, the actual share of employment in "Finance, Insurance, Real Estate, and Business Services"

in Luxembourg is higher than the optimal share by 17 percentage points, and the actual share of

employment in "Manufacturing" in Italy is 25 percentage points higher than the optimal share.

"Community, Social, and Personal Services," which is, on average, "too small" according to our

MVE criterion, is at the other extreme. For example, 33% of U.S. workers are employed in "Com-

munity, Social, and Personal Services," whereas in an MVE-consistent world they should be almost

three times as many.

3. Empirical results

This section is split into four subsections. The first (3.1) investigates the effect of finance on the

economy’s growth-volatility profile. The second (3.2) looks at the nature of sectoral reallocation

and addresses various endogeneity issues associated with faster convergence toward the benchmark

industrial allocation. The third (3.3) considers alternative measures of industrial diversification.

The fourth and final one (3.4) presents robust measures of financial development and compares the

effect of financial development to that of financial and trade integration.

3.1. Finance and convergence

The main empirical question addressed in this paper is whether finance accelerates the economy’s

convergence toward the benchmark MVE-implied industrial composition. We report the estimates

of (12) in Table 3. Column (1) reports the estimates from an OLS regression. The regressions

include industry and year dummy interactions because we want to net out any demand-driven or

technology-driven industry-specific trends. They also include a set of country and industry dummy

interactions to account for the fact that low-volatility sectors can be a superior good (Koren and

Tenreyro, 2007), or that sectors with a higher initial distance can experience faster convergence. The

14

estimate of the direct autoregressive coeffi cient on distance to frontier so defined, α, implies a yearly

reduction of around 3.6% in our sample. Crucially, financial development interacts negatively with

distance to frontier, as implied by the estimate of the coeffi cient β. Our estimates thus suggest

that financial development has a positive effect on the speed with which countries converge to

their effi ciency frontier. Numerically, holding initial distance to frontier constant, a two-standard-

deviation increase in financial development results in an increase of about 0.6% in the speed of

convergence toward the frontier. The estimate is significant at the 5% statistical level.

In Column (2), we estimate (12) using a GMM Arellano-Bond (1991) estimator rather than a

OLS procedure. We do so to account for the presence of a lagged dependent variable in dynamic

panel data. In unreported regressions, we also estimate the GMM estimator introduced by Blundell

and Bond (1998); doing so corrects for the bias arising in fixed effects estimations in dynamic models.

This correction is standard in panel estimation of the finance and growth nexus (e.g., Bonfiglioli,

2008; Acharya et al., 2011). Our main result continues to hold, and the estimate is significant at

the 1% statistical level.

An immediate caveat is that the benchmark allocation of output itself may have been affected

by financial development. If finance affects both growth and volatility, as the literature on finance

and growth has argued, then initial financial underdevelopment will result in artificially low early

growth and high early volatility. Structural breaks in financial development, therefore, will remove

constraints to growth and lower volatility, and that would effectively contaminate our long-term

benchmark. Koren and Tenreyro (2007) argue that the same global sectoral shock will have a lower

aggregate effect in financially developed economies because they have the infrastructure in place

which allows them to hedge against such shocks. By this rationale, financial development can affect

not just the speed of convergence toward an MVE frontier but also long-term labor productivity

growth and volatility, and hence the frontier itself.

One solution is to calculate a "clean" frontier in which long-term labor productivity growth,

volatility, and correlations have not been affected by finance midcycle. In the first column of Table

4, we repeat the empirical tests reported in Table 3, but this time we estimate (12) on a restricted

sample of countries that liberalized domestic credit markets before the beginning of the sample

period. In this way we make sure that we are measuring convergence toward an allocative effi ciency

benchmark based on unconstrained long-term growth and volatility, and not to one contaminated

15

by the initial underdevelopment of financial markets. The estimate of the speed of convergence is

once again significant at the 5% statistical level, and the magnitude of the coeffi cients is if anything

marginally higher than that implied by the estimates from the full sample.

In Column (2) of Table 4, we perform another version of this test. Namely, for all countries

for which data are avaiable over a suffi ciently long period of time, we calculate two separate MVE

frontiers, one for 1970—1988 and another for 1989—2007. Labor productivity growth, volatility, and

correlations are thus calculated over two 19-year periods. The sample split also roughly coincides

with the start of the structural shift toward lower aggregate volatility known as the "Great Modera-

tion." Finance continues to exert a significant effect on the speed of convergence toward benchmark

industrial composition even for this alternative construction of the MVE frontier.

In all, Tables 3 and 4 imply that part of the effect of finance is a restructuring of output towards

sectors which are far from their optimal weight. This process partially captures the effect of finance

on the natural disappearance of obsolete sectors. In theory it could be that the total effect depends

on initial conditions, and so the overall effect of finance is confounded by a very ineffi cient initial

sectoral allocation, limiting the effect of diversification as in Acemoglu and Zilibotti (1997). The

effect of finance also could be confounded by other political economy forces, for instance, large

ineffi cient sectors might be using lobbying tools to acquire government resources and continue

existing while their implied weight might be zero. We investigate these possibilities later on.

It is important to point out that finance has a direct positive effect on the distance to the

optimal industrial benchmark. This implies that close to the frontier (Dc,s,t → 0), more finance is

associated with divergence from the frontier rather than with convergence toward the frontier. At

the same time, this effect is not statistically significant in the OLS case.

3.2. Addressing the endogeneity of finance

We have so far established a positive correlation between financial development and convergence

toward a benchmark allocation of industrial output defined in the sense of mean-variance effi ciency.

However, we have left the question of causality largely unanswered. Given the evidence so far,

the argument can still be made that financial development and diversification are simultaneously

driven by factors unobservable to the econometrician. For example, the uncovered empirical pattern

could be due to the fact that more optimally diversified economies consist of large capital-intensive

16

sectors, which in turn need a large financial industry. Alternatively, unobservable factors such

as the propensity to save, might be driving both the size of financial markets and diversification

patterns. In this subsection, we discuss strategies whereby we deal with these concerns.

3.2.1. The nature of reallocation: Which sectors converge faster?

We first address the issue of omitted variable bias by employing the methodology first introduced

by Rajan and Zingales (1998). They document the significance of the interaction term between a

country-specific component of financial development and an industry-specific component of financial

dependence. The innovation of the method is in that they use a U.S. benchmark to construct an

exogenous measure of financial dependence in their sample of countries that excludes the United

States. This empirical strategy alleviates concerns about the ability of financial development to

anticipate growth, volatility, or the extent of industrial diversification. It also addresses questions

about the joint determination of financial development and growth by a third, unobservable factor.

A natural channel via which we expect finance to exert a causal effect on convergence toward

the frontier is the sector’s natural dependence on external finance. The idea is that financial

development is more likely to reallocate investment towards a sector that needs to become larger in

an MVE sense if this sector is naturally sensitive to developments in financial markets. Empirically,

firms in such sectors are likely to finance a large share of their operating expenses with external

funds (Rajan and Zingales, 1998). Such sectors are also likely to exhibit a high share of small and

young firms in equilibrium (e.g., Klapper et al., 2006; Aghion et al., 2007; Acharya et al., 2011).

We proceed to constructing industry benchmarks that capture these technological characteris-

tics. As our benchmark for external dependence, we look at mature Compustat firms in the United

States, and we take industry median value of the sum across years of total capital expenditures

(Compustat item #128) minus cash flow from operations, that is revenues minus nondepreciation

costs (Compustat item #110), plus decreases in inventories and accounts receivable, plus increases

in accounts payable. While this is clearly not only a measure of the industry’s "natural" demand

for credit but also one of dependence on other sources of external finance, like the corporate bonds

market, Cetorelli and Strahan (2006) show that this benchmark is very highly correlated (ρ = 0.51)

with actual use of bank finance by firms. This feature plus the fact that it is not skewed by con-

straints on the supply side makes the benchmark a powerful instrument for sensitivity to the supply

17

of credit. As a second benchmark for sensitivity to financial development, we calculate the share

of young firms (less than two years old) for each sector using data from the Dun and Bradstreet

database, averaged for 1985-1995.

In Table 5, we re-estimate an updated version of (12), whereby we split the industries in the

sample into low and high, in terms of external dependence (Columns (1) and (2)) and in terms

of the share of young firms (Columns (3) and (4)). The estimates strongly imply that the results

recorded so far apply mostly to the subsample of industries with a high dependence on external

finance (Column (2)) and for industries with a high share of young firms (Column (4)). In the case

of industries with low dependence on external finance (Column (1)) and of industries with a low

share of young firms (Column (3)), the effect of finance on convergence toward the MVE frontier

is not significant, albeit being still negative.

3.2.2. Reversed causality

We now proceed to addressing the issue of reversed causality. For example, countries can demand

larger financial sectors if they are better diversified and at the same time derive a larger share of

economic output from more capital-intensive industries. While this alternative explanation suggests

that a reverse mechanism to the one we argue for is at play, it would still imply that the mean-

variance framework is empirically relevant when it comes to understanding the relation between

financial development and the specialization of production.

Nevertheless, we now proceed to check if the data provide empirical justification for this alter-

native story. We do so in Table 6, where we present estimates from a number of tests aimed at

addressing the issue of reversed causality. First, we replace our preferred measure of financial de-

velopment with liberalization dates of domestic credit markets, as per Appendix Table 1. Although

the argument sometimes has been made that liberalization can be endogenous as policy makers

can undertake it when the country is already starting on the path of higher growth,14 a policy

measure is more exogenous to growth opportunities than is the volume measure we have used so

far. Hence, we replace the financial proxy in (12) with a dummy variable equal to one after the year

in which the country liberalized its credit markets. We find that countries have been converging

to the MVE frontier faster in the years after credit markets liberalization (Column (1)), and this

14See Bekaert et al. (2007) for details.

18

effect is significant at the 10% statistical level.

Another issue with our tests so far is that the financial sector is included both on the left-hand

side and on the right-hand side of the estimation equation. To address this concern, in Column (2)

we exclude the SIC 1-digit sector "Finance, Insurance, Real Estate, and Business Services" from

the main tests. As argued before, our previous results might be biased by the fact that the proxies

used for financial development increase simultaneously alongside the share of financial services on

the left-hand side. The effect of credit market development, however, survives this procedure and

is still significant at the 5% statistical level.

An alternative way through which this mechanism would manifest itself in the data is if one

observed only capital-intensive sectors converging to their optimal weights, but not the labor-

intensive sectors. We address this issue in Column (3). In particular, we estimate a version of (12),

where we have dropped the top three sectors in terms of capital intensity; these are the sectors in

which labor compensation accounts for less than two-thirds of total production.15 The evidence

suggests that capital-intensive industries converge to their optimal MVE-implied weight faster than

the labor-intensive industries, as excuding the most capital-intensive sectors reduces the magnitude

of the overall effect. Nevertheless, labor-intensive industries converge as well, implying that our

results are not solely driven by a mechanism whereby more diversified economies derive a larger

share of output from capital-intensive sectors.

Taken together, the estimates reported in Tables 5 and 6 point to the fact that while valid

arguments can be made that our results are driven by omitted variable bias or by reversed causality,

the positive effect of financial development on the speed of convergence toward a more diversified

industrial composition in an MVE sense survives when we explicitly address these concerns.

3.3. Optimal vs. "naive" diversification

The virtue of our benchmark allocation of industrial output, based on the concept of mean-

variance effi ciency, is that it accounts simultaneously for labor productivity growth, volatility, and

cross-sector correlations. In Table 7, we now contrast our results with those obtained by assuming

away the importance of cross-sector correlations. In the first case, we estimate a benchmark frontier

in which all covariance terms are set to zero. This transforms a mean-variance effi ciency argument

15To calculate labor and capital intensities, we use the industry distribution of the annual ratio of total compensationto industrial production reported by Palacios (2011).

19

into one in which finance targets sectors based solely on their individual Sharpe ratios. Such a

framework fails to explain the full pattern of convergence of sector shares over time (Column (1)).

This implies that the effect of finance on diversification is significant only when the covariance of

returns is properly accounted for in an optimal portfolio sense. This point is important: our results

show that financial development results in lower aggregate volatility not just through a reduction

in intrasectoral volatility as in Braun and Larrain (2005) and Raddatz (2006) but also through

a reallocation of resources away from sectors whose labor productivity growth pattern is highly

correlated with the growth pattern of the rest of the economy.

Next, we contrast our measure of diversification based on allocative effi ciency with a measure

that defines diversification as an equal allocation of employment across sectors, 1N . This measure

constitutes a "naive" concept of diversification, which ignores any considerations about growth,

volatility, and cross-sector correlations. The corresponding measure of distance to frontier in this

case is defined as Dc,s,t =∣∣∣lc,s,t− 1

N

∣∣∣, where N = 9. Column (2) reports the estimates of the

coeffi cient on the interaction term in (12), where Dc,s,t is calculated using this alternative approach.

The result suggests that financial development has a negative effect on the speed with which

the country allocation of output converges to a benchmark in which output is equally spread

across the set of sectors available. However, this effect is only significant at the 10%, suggesting

that reallocation toward an MVE benchmark is a more powerful force at play than mechanical

diversification towards a uniformly diversified industrial portfolio.

An immediate interpretation for this result is in the spirit of the U-shaped diversification pattern

over the development path documented by Imbs and Wacziarg (2003). Our finding that for a

set of industrialized economies, employment is reallocated as to minimize the volatility of labor

productivity growth is not inconsistent with a development pattern in which in later stages the

economy specializes to exploit pecuniary externalities and economies of scale. At the same time, the

fact that economies with more effi cient financial markets do not exhibit a more equal reallocation of

resources across sectors is also consistent with a development path in which "naive" diversification

does not evolve linearly over time.

Finally, we address the fact that volatility is bound from below by the minimum-variance

portfolio (the tip of the mean-variance effi cient frontier). Consequently, distance to the mean-

variance frontier is bounded above by distance to the mean-variance portfolio. We note that for

20

a number of countries, for a number of years, our measure of distance to frontier coincides with

distance to the mean-variance portfolio. This portfolio is in some sense the most low-volatility one,

and the distance to it simply measures the extent of diversification. In Column (3) we report the

estimates from (12), where Dc,s,t is calculated as the distance between each sector’s actual share of

the overall economy and its optimal share in the minimum-variance portfolio. The results confirm

that financial development affects the speed of convergence toward the frontier using this metric,

but the effect is not significant in the statistical sense.

3.4. Robustness: Alternative measures of financial development, trade, and financial integration

So far we have relied exclusively on the time series of the ratio of private credit to GDP to

capture the country-specific evolution in financial depth. There are a number of problems with

this measure that we now address in Table 8. For one, our main proxy for financial development

excludes credit issued by the central bank. Such credit can be driven by political rather than

economic consideration, but at the same time, central bank credit can be less endogenous to the

evolution of industrial specialization. In Column (1), we re-estimate our main tests after adding the

ratio of central bank credit to GDP to our main measure of private credit to GDP. The estimates

are almost identical to those reported in Table 3; this is likely due to the fact that the ratio of

central bank credit to GDP is on average very low in the sample (sample mean of 0.04).

Next, given the importance of access to increasingly international capital markets, especially for

some sectors, alternative measures of financial development that capture the international supply

of capital beg to be considered. In the next two columns, we replace our proxy for financial depth

with measures of stock and bond market capitalization to GDP. The results are consistent with

the effect of credit markets on reallocation. Both deeper stock markets and deeper bond markets

turn out to be associated with faster convergence toward the benchmark (Columns (2) and (3)),

and the effect in both cases is significant at the 10% level. These findings are broadly in line with

the growth effects in Rajan and Zingales (1998) and the volatility results in Braun and Larrain

(2005), suggesting that there is nothing peculiar about credit market depth as a measure of financial

development.

Next, we pay explicit attention to the fact that countries can also diversify abroad, both in

terms of direct and portfolio investment, and this is likely to be especially important for small,

21

open economies. While in our framework economies by construction cannot "short" a sector, we

can still look at the effect of cross-border diversification. In Columns (4) we replace our measure

of credit market development in (12) with a measure of trade openness (i.e., the ratio of exports

plus imports to GDP). This measure of international integration has a significant effect on the

sectors’ speed of convergence to their MVE-implied optimal employment share. In Column (5)

we replace our measure of credit market development with the ratio of gross and of net foreign

assets and liabilities to GDP, both of which proxy for integration in international financial markets.

We detect a significant association between the former and the speed of convergence toward an

MVE-type industrial benchmark. The results thus suggest that domestic credit market deepening

is not the sole force behind convergence toward a more optimally diversified industrial allocation,

as trade integration and financial integration matter, too.

It is important to know if the effect of financial deepening is not dominated by the effect of

concurrent developments. In Columns (6) and (7), we investigate whether the effect of financial de-

velopment remains in place once the economy’s integration in global markets is properly accounted

for. The evidence suggests that the independent effect of financial deepening survives the inclusion

of both measures of openness in the regression.

Finally, our data allow us to pay specific attention to financial services as a productive sector of

the economy. In particular, some countries can have a comparative advantage in financial services

due to specialization in a particular type of human capital, or due to early specialization in banking

activities. A way to exploit this possibility is to test whether countries with initially relatively large

financial sectors have diversification paths different from countries with initially relatively small

financial sectors. We perform our main tests on these two subsamples of countries, and report the

results in Columns (8) and (9). The estimates imply that deeper financial markets increase the

speed of allocative effi ciency for both types of countries; however, the gain in speed of convergence

is relatively higher for countries that initially specialized to a higher degree in financial services

(Column (9)).

In further robustness exercises, which can be found in the Appendix, we show that the effect of

finance on convergence is robust to controlling for the effect of other characteristics of the business

environment (Appendix Table A2), that it is stronger in countries that are less diversified initially

(Appendix Table A3), and that it is robust to using an economic area rather than a country as

22

the unit of observation (Appendix Table A4), and to including the 2008-2009 global recession in

the sample period (Appendix Table A5). It is also robust to computing the underlying distances

to benchmark industrial composition based on data on the growth of value added rather than on

labor productivity growth (Appendix Table A6).

4. Conclusion

This paper investigates the effect of financial development on the economy’s growth-volatility

profile for a wide cross-section of countries. We document two main findings. First, financial

development is Pareto-improving in the sense of delivering lower aggregate long-term volatility for

the same level of long-term growth. Second, the reduction in aggregate volatility is realized through

a reallocation of resources toward sectors whose growth profile is correlated with the rest of the

economy in a way that gives them large optimal weights in an MVE sense. Thus, we identify a new

channel through which financial development affects aggregate volatility, in addition to reducing the

sectors’own long-term volatility. To the extent that output volatility and consumption volatility

are correlated, our results suggest that financial development can have positive welfare implications

through a reduction in overall economic volatility.

Crucially, our findings do not appear to be driven by a global shift away from volatile sectors

during the Great Moderation, or by the endogeneity of financial development. In particular, our

results survive panel regressions with a rich set of fixed effects, and they are not weakened when we

use exogenous measures of financial development, such as banking deregulation. We also document

that in financially developed countries, sectors converge faster to their optimal share in the industrial

portfolio if they are naturally sensitive to external finance. At the same time, convergence is at play

for both labor-intensive and capital-intensive sectors, weakening concerns that our results are driven

by the fact that finance develops faster in economies dominated by capital-intensive industries.

How should one interpret the fact that financial deepening leads to faster convergence to a lower-

volatility sectoral composition? In theory, one would expect the opposite to be true: as financial

development allows for better risk sharing, the real economy can become more specialized, while the

diversification demanded by the representative investor will come from financial assets. However,

because of the MVE-based measure of diversification we employ throughout the paper, our results

actually do not negate such a mechanism. In fact, we do show that financial deepening does not lead

23

to convergence toward an optimal portfolio, calculated by setting the correlations across sectors

to zero. We argue that finance encourages convergence toward a portfolio in which some sectors

have large MVE-implied weights, whereas other sectors have small MVE-implied weights. To the

extent that the large MVE-implied sectors can be those in which the economy is specializing, the

mechanism we detect can be fully consistent with the risk-sharing function of financial deepening.

We stop short of a number of important extensions. For example, we do not explore whether

our results will stand the test of dynamic measures of allocative effi ciency, incorporating the idea

of expanding technological frontiers a la Acemoglu et al. (2006). Also, due to data limitations, our

sample also only consists of industrialized countries. Unlike standard cross-country cross-industry

studies, which use data on manufacturing output for both developed and developing countries, our

methodology requires output data for all sectors of the economy that are only consistently available

for high-income countries. Using comparable data to investigate the effect of financial development

on the overall growth-volatility profile in low-income economies could provide important insights

into the economic costs – in terms of aggregate output volatility – of financial underdevelopment.

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28

Table 1 Actual and MVE-implied employment shares, by industry.

SIC 1-digit sector Actual share MVE-implied optimal share Sector 1. Agriculture, Hunting, Forestry, and Fishing 0.075 0.082 Sector 2. Mining and Quarrying 0.005 0.015 Sector 3. Manufacturing 0.193 0.171 Sector 4. Electricity, Gas, and Water Supply 0.010 0.020 Sector 5. Construction 0.074 0.073 Sector 6. Wholesale and Retail Trade and Restaurants and Hotels 0.200 0.167 Sector 7. Transport, Storage, and Communications 0.063 0.099 Sector 8. Finance, Insurance, Real Estate, and Business Services 0.112 0.031 Sector 9. Community, Social, and Personal Services 0.269 0.342

Note: This table summarizes actual and optimal employment shares as implied by the MVE criterion used in the paper, for nine SIC 1-digit industries. The underlying industry data on value added and employment are from the STAN Dataset for Industrial Analysis. The sample period is 1970--2007. For the derivation of the optimal employment shares, see Section 2.2. See Appendix Table A7 for data sources.

29

Table 2 Difference between actual and MVE-implied employment shares.

Country

Difference between actual and optimal employment shares, SIC 1-digit industries Sector 1 Sector 2 Sector 3 Sector 4 Sector 5 Sector 6 Sector 7 Sector 8 Sector 9

Australia 0.046 0.011 0.080 0.015 0.027 -0.618 0.070 0.115 0.252 Austria 0.031 -0.009 -0.009 0.010 -0.026 0.149 -0.044 0.093 -0.193 Belgium 0.018 0.004 0.115 0.000 0.061 -0.275 0.043 0.136 -0.100 Canada 0.043 0.010 0.166 -0.010 0.015 -0.092 0.012 -0.013 -0.140 Czech Republic 0.048 0.013 0.158 0.013 0.071 -0.013 0.070 0.107 -0.468 Denmark 0.056 0.000 0.108 0.009 0.066 -0.043 0.028 0.107 -0.327 Finland 0.089 0.000 0.214 0.010 0.075 -0.152 0.072 0.087 -0.398 France 0.034 -0.280 -0.161 0.010 0.014 0.072 0.059 0.136 0.127 Germany 0.024 0.003 0.213 0.000 0.071 -0.405 0.054 0.070 -0.031 Greece -0.231 -0.030 -0.093 0.010 0.004 0.135 0.064 0.076 0.068 Hungary -0.049 0.005 0.229 0.001 0.062 0.131 -0.136 0.041 -0.276 Iceland 0.078 -0.120 0.150 0.010 0.023 -0.129 -0.004 0.110 -0.113 Ireland -0.077 0.003 0.096 -0.010 -0.330 0.027 -0.072 0.110 0.254 Italy -0.208 0.000 0.248 -0.040 0.075 0.184 0.000 0.089 -0.339 Japan -0.864 0.000 0.218 -0.020 0.095 0.225 0.060 0.101 0.178 Korea -0.250 0.004 0.042 -0.010 0.018 0.226 0.050 0.060 -0.152 Luxembourg -0.136 0.000 0.154 -0.010 0.106 -0.378 -0.016 0.166 0.120 Netherlands 0.024 0.000 0.115 0.007 -0.022 -0.019 -0.120 0.153 -0.139 New Zealand 0.009 0.000 -0.025 0.006 0.058 0.266 -0.038 0.082 -0.350 Norway 0.064 0.012 0.159 0.010 0.065 0.179 0.053 0.090 -0.631 Poland -0.573 0.022 0.210 0.019 -0.122 0.096 0.060 0.068 0.222 Portugal 0.033 -0.010 -0.205 0.008 0.000 0.124 0.040 0.045 -0.031 Slovakia 0.055 0.007 -0.047 0.020 0.030 0.171 -0.038 0.049 -0.240 Spain 0.024 0.004 0.106 0.007 -0.087 0.207 -0.051 0.090 -0.299 Sweden 0.042 0.000 0.209 0.010 0.060 0.028 0.057 0.058 -0.473 Switzerland 0.043 -0.020 -0.034 0.010 0.073 0.221 0.053 0.085 -0.430 UK -0.020 0.007 0.060 0.007 0.070 -0.603 0.060 0.148 0.277 U.S. 0.023 0.006 0.156 0.006 0.035 0.220 0.051 0.151 -0.646 Total -0.056 -0.014 0.098 0.002 0.026 -0.014 0.020 0.096 -0.159

Note: This table summarizes the average difference between actual and optimal employment shares as implied by the MVE criterion used in the paper, for nine SIC 1-digit industries. The underlying industry data are from the STAN Dataset for Industrial Analysis. The sample period is 1970--2007. For the derivation of the optimal employment shares, see Section 2.2. Unlike in the empirical exercises throughout the paper, in this table the difference between actual and optimal employment share can be negative. A negative difference implies that the actual weight is lower than the optimal weight, and a positive difference implies the opposite. For the SIC 1-digit industrial classification used in the paper, see Table 1. See Appendix Table A7 for data sources.

30

Table 3 Finance and convergence toward benchmark industrial composition.

(1) (2) OLS GMM

⋅−1,, tscD Credit -0.0063** -0.0359*** (0.0023) (0.0029)

1,, −tscD 0.9640*** 0.9524*** (0.0071) (0.0038)

Credit 0.0002 0.0019*** (0.0004) (0.0005) Observations 6,345 6,039 Country× industry dummies Yes Yes Industry×year dummies Yes Yes

Note: This table reports estimates from fixed effects regressions, where the dependent variable is tscD ,, , calculated according to Equation (12). The sample includes all countries for which the number of years with nonmissing data is at least as large as the number of industries. Estimates come from OLS regressions (Column (1)) and from a GMM procedure which implements the Arellano-Bond estimator to account for the presence of a lagged dependent variable in a dynamic panel model (Column (2)). "Credit" is the ratio of private credit to GDP. The sample period is 1970--2007. Standard errors clustered by industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

31

Table 4 Stationarity and structural breaks issues.

(1) (2) “Clean frontier” countries Sample period splits

⋅−1,, tscD Credit -0.0070** -0.0068* (0.0032) (0.0036)

1,, −tscD 0.9642*** 1.0034*** (0.0104) (0.0052)

Credit -0.0003 0.0002 (0.0004) (0.0003) Observations 4,257 6,075 Country× industry dummies Yes Yes Industry×year dummies Yes Yes

Note: This table reports estimates from fixed effects regressions, where the dependent variable is tscD ,, , calculated according to Equation (12). In Column (1), the sample includes all countries for which the number of years with nonmissing data is at least as large as the number of industries and that liberalized their credit markets before 1970. In Column (2), tscD ,, is estimated after splitting the data in two periods, 1970--1988 and 1989--2007, and estimating an MVE benchmark for each country, over two periods. "Credit" is the ratio of private credit to GDP. The sample period is 1970-2007. Standard errors clustered by industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A1 for credit market liberalization dates. See Appendix Table A7 for data sources.

32

Table 5 Finance and convergence toward benchmark industrial composition: Which sectors converge faster?

(1) (2) (3) (4) External dependence Share young firms Low High Low High

⋅−1,, tscD Credit -0.0078 -0.0060** -0.0074 -0.0048** (0.0071) (0.0017) (0.0051) (0.0022)

1,, −tscD 0.9706*** 0.9561*** 0.9661*** 0.9552*** (0.0088) (0.0044) (0.0073) (0.0121) Credit 0.0002 0.0003 0.0004 0.0001 (0.0009) (0.0003) (0.0011) (0.0001) Observations 2,820 3,525 2,820 3,525 Country× industry dummies Yes Yes Yes Yes Industry×year dummies Yes Yes Yes Yes

Note: The dependent variable in all cases is tscD ,, , calculated according to Equation (12). "Credit" is the ratio of private credit to GDP. "External dependence" is the sector’s average median value of capital expenditures minus cash flows divided by capital expenditures over 1980--1990, for mature Compustat firms. "Share young firms" is the average share of firms younger than two years out of the full population of firms for U.S. industries over 1985--1995. The United States are excluded from the regressions. The sample period is 1970--2007. Standard errors clustered by industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

33

Table 6 Endogeneity of finance.

(1) (2) (3) Credit = Bank

liberalization date Financial sector

excluded Only labor-intensive

industries ⋅−1,, tscD Credit -0.0061* -0.0064** -0.0045**

(0.0037) (0.0026) (0.0021)

1,, −tscD 0.9633*** 0.9633*** 0.9666*** (0.0067) (0.0077) (0.0106)

Credit 0.0003 0.0003 -0.0001 (0.003) (0.0005) (0.0005) Observations 6,345 5,640 4,230 Country× industry dummies Yes Yes Yes Industry×year dummies Yes Yes Yes

Note: The dependent variable in all cases is tscD ,, , calculated according to Equation (12). "Credit" is the ratio of private credit to GDP. "Bank liberalization date" equals one for the years after the country liberalized its domestic credit market, and zero otherwise. The financial sector (SIC industry #8) is excluded from the regression in Column (2). Labor-nonintensive sectors (i.e., sectors where labor compensation accounts for less than two-thirds of total production) are dropped from the regression in Column (3). The sample period is 1970--2007. Standard errors clustered by industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A1 for credit market liberalization dates. See Appendix Table A7 for data sources.

34

Table 7 Alternative measures of diversification.

(1) (2) (3)

Corr = 0 Actual weight – 1/N MVP ⋅−1,tcD Credit -0.0050 -0.0150* -0.0024

(0.0056) (0.0076) (0.0023) Observations 6,345 6,345 6,345 Country× industry dummies Yes Yes Yes Industry×year dummies Yes Yes Yes

Note: The dependent variable is tscD ,, in Column (1), calculated by setting correlations equal to zero in Equation (12), the absolute value of the difference between each sector’s actual employment share and 1/N in Column (2), and tscD ,, in Column (3), calculated as the distance to the minimum variance portfolio in Equation (12). See Section 3.3 for details on how those are calculated. "Credit" is the ratio of private credit to GDP. The sample period is 1970--2007. Standard errors clustered by industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

35

Table 8 Alternative measures of finance.

Note: The dependent variable is tscD ,, , calculated according to Equation (12). "Credit" is the ratio of private credit to GDP. "Credit (private and CB)" is the ratio of the sum of private credit and central bank assets to GDP. "Stock" is the ratio of stock market capitalization to GDP. "Bonds" is the ratio of private plus public bonds to GDP. "Trade" is the ratio of exports plus imports to GDP. "Gross foreign assets" is the ratio of foreign assets plus liabilities to GDP. "Net foreign assets" is the ratio of foreign assets to GDP. In Column (8), the analysis is performed on the countries that fall in the bottom half of the distribution of financial sector share of total value added in the initial year of data availability. In Column (9), the analysis is performed on the countries that fall in the top half of the distribution of financial sector share of total value added in the initial year of data. The sample period is 1970--2007. Standard errors clustered by industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

(1) (2) (3) (4) (5) (6) (7) (8) (9) ⋅−1,, tscD Credit -0.0035* -0.0033* -0.0095 -0.0043*

(0.0021) (0.0019) (0.0092) (0.0022) ⋅−1,, tscD Credit (including CB) -0.0069**

(0.0027) ⋅−1,, tscD Stock -0.0031*

(0.0016) ⋅−1,, tscD Bonds -0.0062*

(0.0036) ⋅−1,, tscD Trade -0.0009* -0.0007*

(0.0005) (0.0004) ⋅−1,, tscD Gross foreign assets -0.0007** -0.0007*

0.0003) (0.0003) ⋅−1,, tscD Net foreign assets 0.0012 -0.0032

(0.0035) (0.0034) Observations 6,120 6,624 6,084 6,525 6,408 6,246 6,165 2,981 2,968 Country× industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry×year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes

36

Figure 1. Benchmark versus actual industrial composition over time, United States.

Note: The graph shows the benchmark allocation of employment across sectors, based on mean-variance efficiency and calculated using the sectors’ long-term labor productivity growth, volatility, and correlations over the sample period, and the actual realization of sectoral employment, for the United States. The sample period is 1971--2007.

37

Figure 2. Benchmark versus actual industrial composition over time, euro area.

Note: The graph shows the benchmark allocation of employment across sectors, based on mean-variance efficiency and calculated using the sectors’ long-term labor productivity growth, volatility, and correlations over the sample period, and the actual realization of sectoral employment, for the euro area. The sample period is 1991--2007.

38

Figure 3. Benchmark versus actual industrial composition over time, Japan.

Note: The graph shows the benchmark allocation of employment across sectors, based on mean-variance efficiency and calculated using the sectors’ long-term labor productivity growth, volatility, and correlations over the sample period, and the actual realization of sectoral employment, for Japan. The sample period is 1971--2007.

39

Figure 4. Speed of convergence toward benchmark industrial composition and initial private credit/GDP.

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Note: The graph shows each individual country’s autoregressive annual speed of convergence for the 1970--2007 sample period to the benchmark industrial allocation against beginning-of-sample period private credit to GDP. See Section 2 for details on the derivation of the benchmark industrial allocation.

40

Figure 5. Final distance toward benchmark industrial composition and initial private credit/GDP.

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Note: The graph shows each individual country’s final distance to MVE frontier for the 1970--2007 sample period against beginning-of-sample period private credit to GDP. See Section 2 for details on the derivation of the benchmark industrial allocation.

Appendix Table A1 Credit markets: Private credit / GDP and banking sector liberalization events.

Country Credit markets

Private credit / GDP Liberalization date Australia 0.513 1994 Austria 0.841 <1970 Belgium 0.433 <1970 Canada 0.783 <1970 Czech Republic 0.507 1994 Denmark 0.501 1994 Finland 0.571 <1970 France 0.713 <1970 Germany 1.077 <1970 Greece 0.371 1987 Hungary 0.299 1994 Iceland 0.541 <1970 Ireland 0.821 <1970 Italy 0.618 <1970 Japan 1.452 1985 Korea 0.827 1998 Luxembourg 1.054 <1970 Netherlands 1.069 <1970 New Zealand 0.558 1987 Norway 0.869 1985 Poland 0.236 1994 Portugal 0.856 1986 Slovakia 0.504 1994 Spain 0.811 <1970 Sweden 0.956 1985 Switzerland 1.601 <1970 UK 0.653 <1970 U.S. 1.306 1985

Note: This table describes the main financial variable used in the text, private credit over GDP. Column (1) lists the country-level ratio of private credit by all financial institutions, excluding central banks, to GDP, averaged over the sample period. Column (2) lists the year in which the respective country liberalized its banking sector; "<1970" means that those countries’ credit markets are open throughout the period. The sample period is 1970--2007. See Appendix Table A7 for data sources.

Appendix Table A2 Finance, law, and institutions.

(1)

⋅−1,, tscD Credit -0.0060** (0.0024)

⋅−1,, tscD Entry time -0.0001 (0.0004)

⋅−1,, tscD Investor protection -0.0005 (0.0031)

⋅−1,, tscD Contract enforcement 0.0001 (0.0001)

1,, −tscD 0.9631*** (0.0193) Observations 5,949 Country× industry dummies Yes Industry×year dummies Yes

Note: The dependent variable is tscD ,, , calculated according to Equation (12). "Credit" is the ratio of private credit to GDP. "Entry time" is the number of days necessary to start a business in the respective country. "Investor protection" is an average of three indices of degree of protecting private investors. "Contract enforcement" is the number of days necessary to settle a contractual dispute in court. The sample period is 1970--2007. Standard errors clustered by country-industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

Appendix Table A3 Finance and stages of diversification.

(1) (2) Low initial diversification High initial diversification

⋅−1,, tscD Credit -0.0142* -0.0020 (0.0082) (0.0051)

1,, −tscD 0.9732*** 0.9586*** (0.0076) (0.0128) Credit 0.0004 0.0001 (0.0007) (0.0008) Observations 3,256 3,089 Country× industry dummies Yes Yes Industry×year dummies Yes Yes

Note: The dependent variables is tscD ,, , calculated according to Equation (12). "Credit" is the ratio of private credit to GDP. "Low initial diversification" refers to the countries which are in the bottom half of the allocative-efficiency implied diversification distribution in the first year of data availability. "High initial diversification" refers to the countries which are in the top half of the allocative-efficiency implied diversification distribution in the first year of data availability. The sample period is 1970--2007. Standard errors clustered by country-industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

Appendix Table A4 Finance and convergence toward benchmark industrial composition in larger economic zones.

Note: The dependent variable is tscD ,, , calculated according to Equation (12), using aggregated data for the 12 original euro zone countries, at the SIC 1-digit level of disaggregation. "Credit" is the ratio of private credit to GDP for the 12 original euro zone countries. Column (1) reports OLS regression estimates. Column (2) reports the estimates from a GMM procedure that implements the Arellano-Bond estimator to account for the presence of a lagged dependent variable in a dynamic panel model. The financial sector (SIC industry #8) is excluded from the regression in Column (5). In Column (4), the credit variable has been instrumented using an indicator variable equal to 1 after 1999 (the year of the introduction of the euro). The sample period is 1991--2007. Standard errors clustered by industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

(1) (2) (3) (4)

OLS

Arellano - Bond Financial sector

excluded

2SLS ⋅−1,, tscD Credit -0.0201* -0.0161 -0.0362** -0.0215

(0.0106) (0.0157) (0.0172) (0.0212)

1,, −tscD 0.9648*** 0.9234*** 0.9290*** 0.9658*** (0.0207) (0.0181) (0.0465) (0.0252) Observations 135 126 120 135 Industry dummies Yes Yes Yes Yes Year dummies Yes Yes Yes Yes

Appendix Table A5 Finance and convergence toward benchmark industrial composition: The impact of the 2008--2009 crisis.

(1) (2) OLS GMM

⋅−1,, tscD Credit -0.0040** -0.0794*** (0.0017) (0.0060)

1,, −tscD 0.9499*** 0.9576*** (0.0092) (0.0078)

Credit 0.0002 0.0053*** (0.0004) (0.0008) Observations 6,543 6,237 Country× industry dummies Yes Yes Industry×year dummies Yes Yes

Note: This table reports estimates from fixed effects regressions where the dependent variable is tscD ,, , calculated according to Equation (12). The regressions are carried out on the sample of all countries for which the number of years with nonmissing data is at least as large as the number of industries. "Credit" is the ratio of private credit to GDP. Estimates are from OLS regressions (column labelled "OLS") and from a GMM procedure that implements the Arellano-Bond estimator to account for the presence of a lagged dependent variable in a dynamic panel model (column labelled "GMM"). The sample period is 1970--2009. Standard errors clustered by country-industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

Appendix Table A6 Finance and convergence toward benchmark industrial composition: Using value-added growth data to compute the industry benchmark.

(1) (2) (3) (4) All countries Clean frontier OLS GMM OLS GMM

⋅−1,, tscD Credit -0.0326*** -0.1382*** -0.0590*** -0.1688*** (0.0092) (0.0078) (0.0208) (0.0134)

1,, −tscD 0.9270*** 0.8377*** 0.9398*** 0.8796*** (0.0449) (0.0174) (0.0576) (0.0154)

Credit 0.0018** 0.0043** 0.0024 -0.0011 (0.0009) (0.0018) (0.0015) (0.0030) Observations 6,633 6,498 4,059 3,731 Country× industry dummies Yes Yes Yes Yes Industry×year dummies Yes Yes Yes Yes

Note: This table reports estimates from fixed effects regressions, where the dependent variable is tscD ,, . It is calculated according to Equation (12), where we use data on growth in value added, rather than on growth in value added per worker. The regressions are carried out on the sample of all countries for which the number of years with nonmissing data is at least as large as the number of industries (Columns (1) and (2)), and on the subsample of countries that liberalized their credit markets before the sample period (Columns (3) and (4)). "Credit" is the ratio of private credit to GDP. All estimates are from OLS regressions (columns labelled "OLS") and from a GMM procedure which implements the Arellano-Bond estimator to account for the presence of a lagged dependent variable in a dynamic panel model (columns labelled "GMM"). The sample period is 1970--2007. Standard errors clustered by country-industry appear below each coefficient in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. See Appendix Table A7 for data sources.

Appendix Table A7 Variables and sources.

Value added Total sectoral value added, per country. Available for 9 SIC 1-digit industries for

28 OECD countries, at best staring in 1970. Constructed by deflating nominal growth rates. Source: STAN Database for Structural Analysis.

Employment Total sectoral employment, per country. Available for 9 SIC 1-digit industries

for 28 OECD countries, at best staring in 1970. Source: STAN Database for Structural Analysis.

External dependence The sector’s median value of capital expenditures minus cash flows divided by

capital expenditures for 1980--1990, for mature Compustat firms. Source: Compustat.

Share young firms Share of firms younger than 2 years out of the total population of firms, for U.S.

corporations. Calculated for 1-digit SIC industries. Average for the years 1985--95. Source: Dun & Bradstreet.

Labor intensity The median ratio of total compensation to industrial production in the United

States over 1947--1997. Source: Palacios (2011). Credit The value of total credits by financial intermediaries to the private sector in each

country, available with annual frequency. Excludes credit by central banks. Calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDP_t/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Source: Beck et al. (2013).

Credit (including CB) The value of total credits by financial intermediaries to the private sector in each

country, available with annual frequency, including credit by central banks. Calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDP_t/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Source: Beck et al. (2013).

Stock Value of listed shares to GDP, calculated using the following deflation method:

{(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is stock market capitalization, P_e is end-of period CPI, and P_a is average annual CPI. Source: Beck et al. (2013).

Bonds Private domestic debt securities issued by financial institutions and corporations

plus public domestic debt securities issued by government as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is amount outstanding of private plus public domestic debt securities, P_e is end-of period CPI, and P_a is average annual CPI. Source: Beck et al. (2013).

Trade The sum of exports and imports of the total economy over GDP. Available for

28 OECD countries, at best staring in 1970, with annual frequency. Source: Penn World Tables.

Gross foreign assets The sum of total foreign assets and liabilities over GDP, with annual frequency.

Source: Lane and Milesi-Ferretti (2007). Net foreign assets Total foreign assets over GDP, with annual frequency. Source: Lane and Milesi-

Ferretti (2007).

Bank liberalization Dummy variable equal to 1 after the year in which domestic credit markets were open to foreign participation. Source: Bekaert et al. (2005).

Entry time The time (in days) it takes to register a new business entity in the respective

country. Data aggregated over the time period. Source: Doing Business Database.

Investor protection Average of three indices of protection of investors: transparency of transactions,

liability for self-dealing, and shareholders’ ability to sue officers and directors for misconduct. Data aggregated over the time period. Source: Doing Business Database.

Contract enforcement Number of days it takes to resolve a contractual dispute in the respective

country. Data aggregated over the time period. Source: Doing Business Database.